Integrating remote sensing, GIS and dynamic models: Cellular automata approach for the simulation of urban growth for the city of Montreal

This paper presents a cellular automata approach for modeling the dynamics of urban growth. Cities are among the most complex spatial systems and modeling their dynamics of growth using traditional modeling techniques is a challenging task. Cellular automata (CA) have been widely used for modeling urban growth because of their computational simplicity and explicit representation of time and space. CA models are able to generate complex patterns from the interaction of simple components of the system using simple rules. This makes CA suitable for modeling the complexity of urban dynamics of the cities. Integrating GIS tools and remote sensing data with CA has the potential to provide realistic simulation of the future urban growth of cities. The proposed approach is applied to model the growth of the City of Montreal, Quebec over the last three decades. Land use/land cover maps derived from Landsat data acquired in 1975 and 1990 were used to train a CA model which was then used to project the land use in 2005. A comparison of the projected and actual land uses for 2005 (map also derived from Landsat imagery) is presented and discussed.